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bioinformatics for proteomics
lennart martens
[email protected] omics and systems biology groupVIB / Ghent University, Ghent, Belgium
www.compomics.com@compomics
Sequencial search algorithms
A key issue is to choose the right database
Decoys and false discovery rate calculation
Database search algorithms
Introduction: MS/MS spectra and identification
Protein inference: bad, ugly, and not so good
Sequencial search algorithms
A key issue is to choose the right database
Decoys and false discovery rate calculation
Database search algorithms
Introduction: MS/MS spectra and identification
Protein inference: bad, ugly, and not so good
NH2 CH
CO COOHNH
R1 R2
CH
CO NH
CH
CO NH
CH
R3 R4
x3 y3 z3 x2 y2 z2 x1 y1 z1
a1 b1 c1 a2 b2 c2 a3 b3 c3
There are several other ion types that can be annotated, as well as‘internal fragments’. The latter are fragments that no longer contain an intactterminus. These are harder to use for ‘ladder sequencing’, but can still be interpreted.
This nomenclature was coined by Roepstorff and Fohlmann (Biomed. Mass Spec., 1984) and Klaus Biemann (Biomed.Environ. Mass Spec., 1988) and is commonly referred to as ‘Biemann nomenclature’. Note the link with the Roman alphabet.
Peptides subjected to fragmentation analysis can yield several types of fragment ions
L E N N A R T
L LE
LEN
LENN
LENNA
LENNAR
LENNART
E N N A R TL
T
RT
ART
NARTNNART
ENNART
LENNART
m/z
intensity
In an ideal world, the peptide sequence will produce directly interpretable ion ladders
Real spectra usually look quite a bit worse
LE
LENLENNA LENNART
m/z
intensity
[EL][EI]
[E[IL]][QN][KN]
TART
NARTLENNART
LE/EL N NA / AN[AN][QG][KG]
N
Spectral comparison
Sequencial comparison
Threading comparison
database sequence theoreticalspectrum
experimentalspectrum
compare
database sequence experimentalspectrum
compare de novosequence
database sequence experimentalspectrum
thread
We can distinguish three typesof M/MS identification algorithms
Eidhammer, Wiley, 2007
Sequencial search algorithms
A key issue is to choose the right database
Decoys and false discovery rate calculation
Database search algorithms
Introduction: MS/MS spectra and identification
Protein inference: bad, ugly, and not so good
in silicoMS/MS
in silicodigest
protein sequence database
YSFVATAER
HETSINGK
MILQEESTVYYR
SEFASTPINK
…
peptide sequences
m/z
Int
m/z
Int
m/z
Intm/z
Int
theoretical MS/MSspectra
experimentalMS/MS spectrum
in silicomatching
1) YSFVATAER 342) YSFVSAIR 123) FFLIGGGGK 12
peptide scores
Database search engines match experimental spectra to known peptide sequences
CC BY-SA 4.0
• SEQUEST (UWashington, Thermo Fisher Scientific)http://fields.scripps.edu/sequest
• MASCOT (Matrix Science)http://www.matrixscience.com
• X!Tandem (The Global Proteome Machine Organization)http://www.thegpm.org/TANDEM
Three popular algorithms can serve as templates for the large variety of tools
• Can be used for MS/MS (PFF) identifications
• Based on a cross-correlation score (includes peak height)
• Published core algorithm (patented, licensed to Thermo), Eng, JASMS 1994
• Provides preliminary (Sp) score, rank, cross-correlation score (XCorr),
and score difference between the top tow ranks (deltaCn, ∆Cn)
• Thresholding is up to the user, and is commonly done per charge state
• Many extensions exist to perform a more automatic validation of results
SEQUEST is the original search engine, but not that much used anymore these days
XCorr = deltaCn= XCorr1− XCorr 2
XCorr1𝑅𝑅0 −
1151 �
𝑖𝑖=−75
+75
𝑅𝑅𝑅𝑅
𝑅𝑅𝑖𝑖 = �𝑗𝑗=1
𝑛𝑛
𝑥𝑥𝑗𝑗 � 𝑦𝑦(𝑗𝑗+𝑖𝑖)
From: MacCoss et al., Anal. Chem. 2002
From: Peng et al., J. Prot. Res.. 2002
SEQUEST reveals the problems with scoring different charges, and using different scores
• Very well established search engine, Perkins, Electrophoresis 1999
• Can do MS (PMF) and MS/MS (PFF) identifications
• Based on the MOWSE score,
• Unpublished core algorithm (trade secret)
• Predicts an a priori threshold score that identifications need to pass
• From version 2.2, Mascot allows integrated decoy searches
• Provides rank, score, threshold and expectation value per identification
• Customizable confidence level for the threshold score
Mascot is probably the most recognized search engine, despite its secret algorithm
• A successful open source search engine, Craig and Beavis, RCMS 2003
• Can be used for MS/MS (PFF) identifications
• Based on a hyperscore (Pi is either 0 or 1):
• Relies on a hypergeometric distribution (hence hyperscore)
• Published core algorithm, and is freely available
• Provides hyperscore and expectancy score (the discriminating one)
• X!Tandem is fast and can handle modifications in an iterative fashion
• Has rapidly gained popularity as (auxiliary) search engine
X!Tandem is a clear front-runneramong open source search engines
*0
* !* !n
i i b yi
HyperScore I P N N=
= ∑
-10
-8
-6
-4
-2
0
2
4
6
0 20 40 60 80 100
hyperscore
log(
# re
sults
)
log(
# re
sults
)
0
0.5
1
1.5
2
2.5
3
3.5
4
20 25 30 35 40 45 50
hyperscore0
10
20
30
40
50
60
0 20 40 60 80 100
hyperscore
# re
sults
Adapted from: Brian Searle, ProteomeSoftware,http://www.proteomesoftware.com/XTandem_edited.pdf
significancethreshold
E-value=e-8.2
X!Tandem’s significance calculation for scores can be seen as a general template
The influence of various parameter changes on database size is clearly visible
Verheggen, Mass Spec Reviews, 2017
And the effect on identification rateis correspondingly obvious
Verheggen, Mass Spec Reviews, 2017
The main search engines in use are Mascot, Andromeda, SEQUEST and X!Tandem
Verheggen, Mass Spec Reviews, 2017
Among the up-and-coming engines, Comet, MS-GF+ and MS-Amanda are most notable
Verheggen, Mass Spec Reviews, 2017
In metaproteomics or proteogenomics combining search algorithms can be useful
Muth and Kolmeder, Proteomics, 2015
SearchGUI makes it very easy for youto run multiple free search engines
Vaudel, Proteomics, 2011
PeptideShaker is your gateway to the results
Vaudel, Nature Biotechnology, 2015
Our brand-new ionbot engine allows youto search for all possible modifications!
https://ionbot.cloud
ionbot searches for:- all 1490 UniMod mods- all possible SAPs
Known modifications from Terman and Kashina, Curr Opin Cell Biol, 2013Source data presented to ionbot from Kim et al., Nature, 2014 and mapping on PDB 3j82
human beta actin, front human beta actin, rear
ionbot recaptiulates a few decades of work on beta actin, and actually expands upon it
An example match with UniProt annotations (for Elongation factor 1-alpha 1) is very good
Peptide Residue UniProt # Mods Modification listTHINIVVIGHVDSGK K20 NO 1 carbamidomethylSTTTGHLIYK K30 NO 1 carbamidomethylCGGIDKR K36 YES 2 dimethyl,methylTIEKFEK K44 NO 1 carbamidomethylGSFKYAWVLDK K55 YES 3 carbamidomethyl,dimethyl,methylGITIDISLWKFETSK K79 YES 2 carbamidomethyl,trimethylYYVTIIDAPGHRDFIK K100 NO 1 carbamidomethylEHALLAYTLGVKQLIVGVNK K146 NO 1 carbamidomethylQLIVGVNK K154 NO 1 carbamidomethylMDSTEPPYSQK K165 YES 4 carbamidomethyl,dimethyl,methyl,trimethylYEEIVKEVSTYIK K172 acetyl* 2 carbamidomethyl,carboxymethylDGNASGTTLLEALDCILPPTRPTDK K244 NO 1 carbamidomethylLPLQDVYKIGGIGTVPVGR K255 NO 2 carbamidomethyl,trimethylVETGVLKPGMVVTFAPVNVTTEVK K273 acetyl* 4 carbamidomethyl,dicarbamidomethyl,dimethyl,trimethylVETGVLKPGMVVTFAPVNVTTEVK K290 NO 2 carbamidomethyl,dicarbamidomethylNVSVKDVR K318 YES 2 dimethyl,trimethylKLEDGPK K392 acetyl* 1 carbamidomethylSGDAAIVDMVPGKPMCVESFSDYPPLGR K408 NO 3 carbamidomethyl,carboxymethyl,methylolQTVAVGVIK K439 acetyl* 1 carbamidomethyl
missing according to UniProt: K2, which is a very short peptide (4 residues)
Known modifications from UniProt entry P68104, https://www.uniprot.org/uniprot/P68104Source data presented to ionbot from Kim et al., Nature, 2014
Sequencial search algorithms
A key issue is to choose the right database
Decoys and false discovery rate calculation
Database search algorithms
Introduction: MS/MS spectra and identification
Protein inference: bad, ugly, and not so good
sequence tag
The concept of sequence tags was introduced by Mann and Wilm
1079.61 - SD[IL] - 303.20
Sequence tags are as old as SEQUEST, and these still have a role to play today
Mann, Analytical Chemistry, 1994
• Tabb, Anal. Chem. 2003, Tabb, JPR 2008, Dasari, JPR 2010
• Recent implementations of the sequence tag approach
• Refine hits by peak mapping in a second stage to resolve ambiguities
• Rely on a empirical fragmentation model
• Published core algorithms, DirecTag and TagRecon freely available
• GutenTag and DirecTag extracts tags,
• TagRecon matches these to the database
• Very useful to retrieve unexpected peptides (modifications, variations)
• Entire workflows exist (e.g., combination with IDPicker)
GutenTag, DirecTag, TagRecon
GutenTag: two stage, hybrid tag searching
Tabb, Analytical Chemistry, 2003
Example of a manual de novo of an MS/MS spectrumNo more database necessary to extract a sequence!
Algorithms
LutefiskSherenga
PEAKSPepNovo
…
References
Dancik 1999, Taylor 2000Fernandez-de-Cossio 2000
Ma 2003, Zhang 2004Frank 2005, Grossmann 2005
…
De novo sequencing tries to read the entire peptide sequence from the spectrum
Sequencial search algorithms
A key issue is to choose the right database
Decoys and false discovery rate calculation
Database search algorithms
Introduction: MS/MS spectra and identification
Protein inference: bad, ugly, and not so good
Colony colapse disorder, soldiers,and forcing the issue (or rather: the solution)
Knudsen, PLoS ONE, 2011
The identification seems reasonable,but is limited in an unreasonable way!
Knudsen, PLoS ONE, 2011
The end result may be that you are takento task for mistakes in your research
Beware of common contaminants
Tyrosine nitrosylation
Ghesquière, Proteomics, 2010
Sequencial search algorithms
A key issue is to choose the right database
Decoys and false discovery rate calculation
Database search algorithms
Introduction: MS/MS spectra and identification
Protein inference: bad, ugly, and not so good
All hits, good and bad together,form a distribution of scores
Nesvizhskii, J Proteomics, 2010
If we know how scores for bad hits distribute, we can distinguish good from bad by score
The separation is not perfect, which leads to the calculation of a local false discovery rate
local false discovery rate(posterior error probability; PEP)
- Reversed databases (easy)
LENNARTMARTENS Æ SNETRAMTRANNEL
- Shuffled databases (slightly more difficult)
LENNARTMARTENS Æ NMERLANATERTTN (for instance)
- Randomized databases (as difficult as you want it to be)
LENNARTMARTENS Æ GFVLAEPHSEAITK (for instance)
Three main types of decoy DB’s are used:
The concept is that each peptide identified from the decoy database is an incorrect identification. By counting the number of decoy hits, we can estimate the number of false positives in the original database, provided that the decoys have similar properties as the forward sequences.
Decoy databases are false positive factories, assumed to deliver representative bad hits
With the help of the scores of decoy hits,we can assess the score distribution of bad hits
local false discovery rate(posterior error probability; PEP)
Käll, Journal of Proteome Research, 2008
score
Setting a threshold classifies all hits as either bad or good, which inevitably leads to errors
True Positive
False Positive
False Negative True Negative
Sequencial search algorithms
A key issue is to choose the right database
Decoys and false discovery rate calculation
Database search algorithms
Introduction: MS/MS spectra and identification
Protein inference: bad, ugly, and not so good
peptides a b c d
proteinsprot X x xprot Y xprot Z x x x
Minimal setOccam {
peptides a b c d
proteinsprot X x xprot Y xprot Z x x x
Maximal setanti-Occam {
peptides a b c d
proteinsprot X (-) x xprot Y (+) xprot Z (0) x x x
Minimal set withmaximal annotation {
true Occam?
Protein inference is a question of conviction
Martens, Molecular Biosystems, 2007
In real life, protein inference issues will bemainly bad, often ugly, and occasionally good
Protein inference is linked to quantification (i)
Nice and easy, 1/1, only unique peptides (blue) and narrow distribution
Colaert, Proteomics, 2010
Nice and easy, down-regulated
Protein inference is linked to quantification (ii)
Colaert, Proteomics, 2010
Protein inference is linked to quantification (iii)
A little less easy, up-regulated
Colaert, Proteomics, 2010
Protein inference is linked to quantification (iv)
A nice example of the mess of degenerate peptides
Colaert, Proteomics, 2010
Protein inference is linked to quantification (v)
A bit of chaos, but a defined core distributionColaert, Proteomics, 2010